Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure.

IF 3.1 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Frontiers in Medicine Pub Date : 2025-02-20 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1497651
Zhongxiang Liu, Bingqing Zuo, Jianyang Lin, Zhixiao Sun, Hang Hu, Yuan Yin, Shuanying Yang
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Abstract

Background: The prognostic prediction of patients with hypercapnic respiratory failure holds significant clinical value. The objective of this study was to develop and validate a predictive model for predicting survival in patients with hypercapnic respiratory failure.

Methods: The study enrolled a total of 697 patients with hypercapnic respiratory failure, including 565 patients from the First People's Hospital of Yancheng in the modeling group and 132 patients from the People's Hospital of Jiangsu Province in the external validation group. The three selected models were random survival forest (RSF), DeepSurv, a deep learning-based survival prediction algorithm, and Cox Proportional Risk (CoxPH). The model's predictive performance was evaluated using the C-index and Brier score. Receiver operating characteristic curve (ROC), area under ROC curve (AUC), and decision curve analysis (DCA) were employed to assess the accuracy of predicting the prognosis for survival at 6, 12, 18, and 24 months.

Results: The RSF model (c-index: 0.792) demonstrated superior predictive ability for the prognosis of patients with hypercapnic respiratory failure compared to both the traditional CoxPH model (c-index: 0.699) and DeepSurv model (c-index: 0.618), which was further validated on external datasets. The Brier Score of the RSF model demonstrated superior performance, consistently measuring below 0.25 at the 6-month, 12-month, 18-month, and 24-month intervals. The ROC curve confirmed the superior discrimination of the RSF model, while DCA demonstrated its optimal clinical net benefit in both the modeling group and the external validation group.

Conclusion: The RSF model offered distinct advantages over the CoxPH and DeepSurv models in terms of clinical evaluation and monitoring of patients with hypercapnic respiratory failure.

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背景:预测高碳酸血症呼吸衰竭患者的预后具有重要的临床价值。本研究旨在开发并验证一个预测模型,用于预测高碳酸血症呼吸衰竭患者的生存率:该研究共纳入 697 例高碳酸血症呼吸衰竭患者,其中建模组为盐城市第一人民医院的 565 例患者,外部验证组为江苏省人民医院的 132 例患者。所选的三个模型分别是随机生存森林(RSF)、基于深度学习的生存预测算法DeepSurv和Cox比例风险(CoxPH)。模型的预测性能采用 C 指数和 Brier 评分进行评估。采用接收者操作特征曲线(ROC)、ROC曲线下面积(AUC)和决策曲线分析(DCA)来评估预测6、12、18和24个月生存预后的准确性:与传统的 CoxPH 模型(c-index:0.699)和 DeepSurv 模型(c-index:0.618)相比,RSF 模型(c-index:0.792)对高碳酸血症呼吸衰竭患者预后的预测能力更强。RSF 模型的 Brier Score 显示出卓越的性能,在 6 个月、12 个月、18 个月和 24 个月的时间间隔内始终低于 0.25。ROC曲线证实了RSF模型的卓越辨别能力,而DCA在建模组和外部验证组中都显示出了最佳临床净效益:RSF模型与CoxPH和DeepSurv模型相比,在高碳酸血症呼吸衰竭患者的临床评估和监测方面具有明显优势。
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来源期刊
Frontiers in Medicine
Frontiers in Medicine Medicine-General Medicine
CiteScore
5.10
自引率
5.10%
发文量
3710
审稿时长
12 weeks
期刊介绍: Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world
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